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Record W3138537787 · doi:10.1299/jsmemecj.2020.j10206

Influence of sound pressure and grazing flow on the acoustic resistance of resonator orifice

2020· article· en· W3138537787 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Proceedings of Mechanical Engineering Congress Japan · 2020
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsBody orificeMach numberAcoustic impedanceDuct (anatomy)AcousticsParticle velocitySound pressureResonatorPhysicsMicrophoneFlow velocityMaterials scienceMechanicsFlow (mathematics)OpticsEngineeringAnatomy

Abstract

fetched live from OpenAlex

Acoustic impedance of a rectangular orifice of a resonator on a side wall of a duct was measured. The measurement was based on two-microphone-method, in which the acoustic impedance was calculated from the transfer function of sound pressures at the end of the resonator and at the outlet of the orifice. The acoustic impedance, especially the resistance θp at resonance frequency fp, depends on the particle velocity mach number undulating through the orifice M0 and the flow speed mach number in the duct grazing over the orifice M. When the grazing flow speed mach number M is small, the resistance θp mainly increases with the particle velocity mach number M0. On the other hand, when M is large, θp increases with M and is almost independent of M0. In the latter state, the sound pressure at the resonator end decreases with M while the sound pressure at the orifice slightly increases with M, which results in the increase in θp. Those two states are roughly classified by use of the ratio M/M0; M/M0 is lower or higher than around 3.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.188
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it